import tensorflow as tf
from tensorflow import keras
import IPython.display as display

import numpy as np
import matplotlib.pyplot as plt

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

train_images = train_images / 255.0
test_images = test_images / 255.0

print(test_images[0])

# # 卷积层结构
# model = keras.Sequential([
#     keras.layers.Flatten(input_shape=(28, 28)),
#     keras.layers.Dense(128, activation='relu'),
#     keras.layers.Dense(10)
# ])
# # 编译
# model.compile(optimizer='adam',
#               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
#               metrics=['accuracy'])
#
# model.fit(train_images, train_labels, epochs=10)
# # 保存权重
# model.save("model")
